OpenRaiser/Intern-Atlas huggingface dataset:
FEDEXCHANGE: Bridging the Domain Gap in Federated Object Detection for Free
Venue: arXiv cs.LG 2025
Year: 2025

Federated Object Detection (FOD) enables clients to collaboratively train a global object detection model without accessing their local data from diverse domains. However, significant variations in environment, weather, and other domain specific factors hinder performance, making cross domain generalization a key challenge. Existing FOD methods often overlook the hardware constraints of edge devices and introduce local training regularizations that incur high computational costs, limiting real-world applicability. In this paper, we propose FEDEXCHANGE, a novel FOD framework that bridges domain gaps without introducing additional local computational overhead. FEDEXCHANGE employs a server side dynamic model exchange strategy that enables each client to gain insights from other clients' domain data without direct data sharing. Specifically, FEDEXCHANGE allows the server to alternate between model aggregation and model exchange. During aggregation rounds, the server aggregates all local models as usual. In exchange rounds, FEDEXCHANGE clusters and exchanges local models based on distance measures, allowing local models to learn from a variety of domains. As all operations are performed on the server side, clients can achieve improved cross domain utility without any additional computational overhead. Extensive evaluations demonstrate that FEDEXCHANGE enhances FOD performance, achieving 1.6X better mean average precision in challenging domains, such as rainy conditions, while requiring only 0.8X the computational resources compared to baseline methods.
An Internet of Intelligent Things Framework for Decentralized Heterogeneous Platforms
Venue: arXiv cs.NI 2025
Year: 2025

Internet of Intelligent Things (IoIT), an emerging field, combines the utility of Internet of Things (IoT) devices with the innovation of embedded AI algorithms. However, it does not come without challenges, and struggles regarding available computing resources, energy supply, and storage limitations. In particular, many impediments to IoIT are linked to the energy-efficient deployment of machine learning (ML)/deep learning (DL) models in embedded devices. Research has been conducted to design energy-efficient IoIT platforms, but these papers often focus on centralized systems, in which some central entity processes all the data and coordinates actions. This can be problematic, e.g., serve as bottleneck or lead to security concerns. In a decentralized system, nodes/devices would self-organize and make their own decisions. Therefore, to address such issues, we propose a heterogeneous, decentralized sensing and monitoring IoIT peer-to-peer mesh network system model. Nodes in the network will coordinate towards several optimization goals: reliability, energy efficiency, and latency. The system employs federated learning to train nodes in a distributed manner, metaheuristics to optimize task allocation and routing paths, and multi-objective optimization to balance conflicting performance goals.
LogGuardQ: A Cognitive-Enhanced Reinforcement Learning Framework for Cybersecurity Anomaly Detection in Security Logs
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning (RL) has transformed sequential decision-making, but traditional algorithms like Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) often struggle with efficient exploration, stability, and adaptability in dynamic environments. This study presents LogGuardQ (Adaptive Log Guard with Cognitive enhancement), a novel framework that integrates a dual-memory system inspired by human cognition and adaptive exploration strategies driven by temperature decay and curiosity. Evaluated on a dataset of 1,000,000 simulated access logs with 47.9% anomalies over 20,000 episodes, LogGuardQ achieves a 96.0% detection rate (versus 93.0% for DQN and 47.1% for PPO), with precision of 0.4776, recall of 0.9996, and an F1-score of 0.6450. The mean reward is 20.34 \pm 44.63 across all episodes (versus 18.80 \pm 43.98 for DQN and -0.17 \pm 23.79 for PPO), with an average of 5.0 steps per episode (constant across models). Graphical analyses, including learning curves smoothed with a Savgol filter (window=501, polynomial=2), variance trends, action distributions, and cumulative detections, demonstrate LogGuardQ's superior stability and efficiency. Statistical tests (Mann-Whitney U) confirm significant performance advantages (e.g., p = 0.0002 vs. DQN with negligible effect size, p < 0.0001 vs. PPO with medium effect size, and p < 0.0001 for DQN vs. PPO with small effect size). By bridging cognitive science and RL, LogGuardQ offers a scalable approach to adaptive learning in uncertain environments, with potential applications in cybersecurity, intrusion detection, and decision-making under uncertainty.
Adaptive Preference Optimization with Uncertainty-aware Utility Anchor
Venue: arXiv cs.LG 2025
Year: 2025

Offline preference optimization methods are efficient for large language models (LLMs) alignment. Direct Preference optimization (DPO)-like learning, one of the most popular approaches, stands out for its efficiency in reward modeling. However, these methods typically follow the convention to use Bradley-Terry (BT) reward modeling that faces several critical assumptions, including the requirement for pairwise training data, model distribution shifting, human rationality assumption, etc. To address these limitations, we propose a general framework for offline preference optimization methods, Adaptive Preference Optimization with Utility Anchor (UAPO), which introduces an anchoring function to estimate the uncertainties brought from preference data annotation. Our method enables training even in scenarios where the data is unpaired, significantly enhancing data utilization efficiency. Moreover, the anchor design makes UAPO more robust in the training process. Experimental results demonstrate that UAPO achieves competitive outcomes without the strict dependency on data pairing, paving the way for more flexible and effective preference optimization methods.
Mitigating Catastrophic Forgetting and Mode Collapse in Text-to-Image Diffusion via Latent Replay
Venue: arXiv cs.LG 2025
Year: 2025

Continual learning -- the ability to acquire knowledge incrementally without forgetting previous skills -- is fundamental to natural intelligence. While the human brain excels at this, artificial neural networks struggle with "catastrophic forgetting," where learning new tasks erases previously acquired knowledge. This challenge is particularly severe for text-to-image diffusion models, which generate images from textual prompts. Additionally, these models face "mode collapse," where their outputs become increasingly repetitive over time. To address these challenges, we apply Latent Replay, a neuroscience-inspired approach, to diffusion models. Traditional replay methods mitigate forgetting by storing and revisiting past examples, typically requiring large collections of images. Latent Replay instead retains only compact, high-level feature representations extracted from the model's internal architecture. This mirrors the hippocampal process of storing neural activity patterns rather than raw sensory inputs, reducing memory usage while preserving critical information. Through experiments with five sequentially learned visual concepts, we demonstrate that Latent Replay significantly outperforms existing methods in maintaining model versatility. After learning all concepts, our approach retained 77.59% Image Alignment (IA) on the earliest concept, 14% higher than baseline methods, while maintaining diverse outputs. Surprisingly, random selection of stored latent examples outperforms similarity-based strategies. Our findings suggest that Latent Replay enables efficient continual learning for generative AI models, paving the way for personalized text-to-image models that evolve with user needs without excessive computational costs.
Pair-Bid Auction Model for Optimized Network Slicing in 5G RAN
Venue: arXiv cs.NI 2025
Year: 2025

Network slicing is a key 5G technology that enables multiple virtual networks to share physical infrastructure, optimizing flexibility and resource allocation. This involves Mobile Network Operators (MNO), Mobile Virtual Network Operators (MVNOs), and end users, where MNO leases network slices to MVNOs, and then provides customized services. This work considers end-to-end network slicing with a focus on fair sharing and financial-related power efficiency, modeled as a two level hierarchical combinatorial auction. At the upper level, an MNO auctions slices to competing MVNOs, while at the lower level, MVNOs allocate resources to end users through their own auctions. Dynamic user requests add complexity to the process. Our model optimizes resource allocation and revenue generation using a pair-bid mechanism and Vickrey-Clarke-Groves (VCG) pricing. The pair-bid approach enhances competition and efficiency, while VCG ensures truthful bidding based on marginal system impact. Simulations validate the model's effectiveness in resource distribution and financial performance, showing a 12.5% revenue improvement over the baseline.
Decoupling the "What" and "Where" With Polar Coordinate Positional Embeddings
Venue: arXiv cs.LG 2025
Year: 2025

The attention mechanism in a Transformer architecture matches key to query based on both content -- the what -- and position in a sequence -- the where. We present an analysis indicating that what and where are entangled in the popular RoPE rotary position embedding. This entanglement can impair performance particularly when decisions require independent matches on these two factors. We propose an improvement to RoPE, which we call Polar Coordinate Position Embeddings or PoPE, that eliminates the what-where confound. PoPE is far superior on a diagnostic task requiring indexing solely by position or by content. On autoregressive sequence modeling in music, genomic, and natural language domains, Transformers using PoPE as the positional encoding scheme outperform baselines using RoPE with respect to evaluation loss (perplexity) and downstream task performance. On language modeling, these gains persist across model scale, from 124M to 774M parameters. Crucially, PoPE shows strong zero-shot length extrapolation capabilities compared not only to RoPE but even a method designed for extrapolation, YaRN, which requires additional fine tuning and frequency interpolation.
DualAlign: Generating Clinically Grounded Synthetic Data
Venue: arXiv cs.LG 2025
Year: 2025

Synthetic clinical data are increasingly important for advancing AI in healthcare, given strict privacy constraints on real-world EHRs, limited availability of annotated rare-condition data, and systemic biases in observational datasets. While large language models (LLMs) can generate fluent clinical text, producing synthetic data that is both realistic and clinically meaningful remains challenging. We introduce DualAlign, a framework that enhances statistical fidelity and clinical plausibility through dual alignment: (1) statistical alignment, which conditions generation on patient demographics and risk factors; and (2) semantic alignment, which incorporates real-world symptom trajectories to guide content generation. Using Alzheimer's disease (AD) as a case study, DualAlign produces context-grounded symptom-level sentences that better reflect real-world clinical documentation. Fine-tuning an LLaMA 3.1-8B model with a combination of DualAlign-generated and human-annotated data yields substantial performance gains over models trained on gold data alone or unguided synthetic baselines. While DualAlign does not fully capture longitudinal complexity, it offers a practical approach for generating clinically grounded, privacy-preserving synthetic data to support low-resource clinical text analysis.
Uncovering the Vulnerability of Large Language Models in the Financial Domain via Risk Concealment
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are increasingly integrated into financial applications, yet existing red-teaming research primarily targets harmful content, largely neglecting regulatory risks. In this work, we aim to investigate the vulnerability of financial LLMs through red-teaming approaches. We introduce Risk-Concealment Attacks (RCA), a novel multi-turn framework that iteratively conceals regulatory risks to provoke seemingly compliant yet regulatory-violating responses from LLMs. To enable systematic evaluation, we construct FIN-Bench, a domain-specific benchmark for assessing LLM safety in financial contexts. Extensive experiments on FIN-Bench demonstrate that RCA effectively bypasses nine mainstream LLMs, achieving an average attack success rate (ASR) of 93.18%, including 98.28% on GPT-4.1 and 97.56% on OpenAI o1. These findings reveal a critical gap in current alignment techniques and underscore the urgent need for stronger moderation mechanisms in financial domains. We hope this work offers practical insights for advancing robust and domain-aware LLM alignment.
Auditable Early Stopping for Agentic Routing: Ledger-Verified Run-Wise Certificates under Local DP
Venue: arXiv cs.CR 2025
Year: 2025

We address when a best-first router for tool-use agents can stop exploring without missing a better leaf, while preserving local differential privacy (LDP) and leaving an audit trail. We introduce a run-wise certificate that couples each node's key to the same exponential race that realizes leaf perturbations; the usual halting rule (stop when the maximum over $v$ in $F$ of Key$(v) \le B^*$) then certifies the realized run. We give two certified modes on context-indexed prefix DAGs with child partition: (i) Exact (known counts), using lazy offset propagation with winner reuse; and (ii) Surrogate (upper bounds only), which anchors keys to a parent-level surrogate race and allows validator tightening via $κ= \log(N / N_{ub}$). A small compiler enforces the partition property, and an admissible, race-independent M(tau) keeps keys sound. The ledger logs uniforms, counts, and tie handling; privacy follows by post-processing. Experiments on synthetic graphs and a small real pipeline show tight stopping, deterministic replay, and low overhead.
AVEC: Bootstrapping Privacy for Local LLMs
Venue: arXiv cs.CR 2025
Year: 2025

This position paper presents AVEC (Adaptive Verifiable Edge Control), a framework for bootstrapping privacy for local language models by enforcing privacy at the edge with explicit verifiability for delegated queries. AVEC introduces an adaptive budgeting algorithm that allocates per-query differential privacy parameters based on sensitivity, local confidence, and historical usage, and uses verifiable transformation with on-device integrity checks. We formalize guarantees using Rényi differential privacy with odometer-based accounting, and establish utility ceilings, delegation-leakage bounds, and impossibility results for deterministic gating and hash-only certification. Our evaluation is simulation-based by design to study mechanism behavior and accounting; we do not claim deployment readiness or task-level utility with live LLMs. The contribution is a conceptual architecture and theoretical foundation that chart a pathway for empirical follow-up on privately bootstrapping local LLMs.
Can any model be fabricated? Inverse operation based planning for hybrid additive-subtractive manufacturing
Venue: arXiv cs.GR 2025
Year: 2025

This paper presents a method for computing interleaved additive and subtractive manufacturing operations to fabricate models of arbitrary shapes. We solve the manufacturing planning problem by searching a sequence of inverse operations that progressively transform a target model into a null shape. Each inverse operation corresponds to either an additive or a subtractive step, ensuring both manufacturability and structural stability of intermediate shapes throughout the process. We theoretically prove that any model can be fabricated exactly using a sequence generated by our approach. To demonstrate the effectiveness of this method, we adopt a voxel-based implementation and develop a scalable algorithm that works on models represented by a large number of voxels. Our approach has been tested across a range of digital models and further validated through physical fabrication on a hybrid manufacturing system with automatic tool switching.
Interpretable neural network system identification method for two families of second-order systems based on characteristic curves
Venue: arXiv cs.LG 2025
Year: 2025

Nonlinear system identification often involves a fundamental trade-off between interpretability and flexibility, often requiring the incorporation of physical constraints. We propose a unified data-driven framework that combines the mathematical structure of the governing differential equations with the flexibility of neural networks (NNs). At the core of our approach is the concept of characteristic curves (CCs), which represent individual nonlinear functions (e.g., friction and restoring components) of the system. Each CC is modeled by a dedicated NN, enabling a modular and interpretable representation of the system equation. To demonstrate the versatility of the CC-based formalism, we introduce three identification strategies: (1) SINDy-CC, which extends the sparse regression approach of SINDy by incorporating the mathematical structure of the governing equations as constraints; (2) Poly-CC, which represents each CC using high-degree polynomials; and (3) NN-CC, which uses NNs without requiring prior assumptions about basis functions. Our results show that all three approaches are well-suited for systems with simple polynomial nonlinearities, such as the van der Pol oscillator. In contrast, NN-CC demonstrates superior performance in modeling systems with complex nonlinearities and discontinuities, such as those observed in stick-slip systems. The key contribution of this work is to demonstrate that the CC-based framework, particularly the NN-CC approach, can capture complex nonlinearities while maintaining interpretability through the explicit representation of the CCs. This balance makes it well-suited for modeling systems with discontinuities and complex nonlinearities that are challenging to assess using traditional polynomial or sparse regression methods, providing a powerful tool for nonlinear system identification.
USCTNet: A deep unfolding nuclear-norm optimization solver for physically consistent HSI reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

Reconstructing hyperspectral images (HSIs) from a single RGB image is ill-posed and can become physically inconsistent when the camera spectral sensitivity (CSS) and scene illumination are misspecified. We formulate RGB-to-HSI reconstruction as a physics-grounded inverse problem regularized by a nuclear norm in a learnable transform domain, and we explicitly estimate CSS and illumination to define the forward operator embedded in each iteration, ensuring colorimetric consistency. To avoid the cost and instability of full singular-value decompositions (SVDs) required by singular-value thresholding (SVT), we introduce a data-adaptive low-rank subspace SVT operator. Building on these components, we develop USCTNet, a deep unfolding solver tailored to HSI that couples a parameter estimation module with learnable proximal updates. Extensive experiments on standard benchmarks show consistent improvements over state-of-the-art RGB-based methods in reconstruction accuracy. Code: https://github.com/psykheXX/USCTNet-Code-Implementation.git
M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations
Venue: arXiv cs.LG 2025
Year: 2025

Mesh-based graph neural networks (GNNs) have become effective surrogates for PDE simulations, yet their deep message passing incurs high cost and over-smoothing on large, long-range meshes; hierarchical GNNs shorten propagation paths but still face two key obstacles: (i) building coarse graphs that respect mesh topology, geometry, and physical discontinuities, and (ii) maintaining fine-scale accuracy without sacrificing the speed gained from coarsening. We tackle these challenges with M4GN, a three-tier, segment-centric hierarchical network. M4GN begins with a hybrid segmentation strategy that pairs a fast graph partitioner with a superpixel-style refinement guided by modal-decomposition features, producing contiguous segments of dynamically consistent nodes. These segments are encoded by a permutation-invariant aggregator, avoiding the order sensitivity and quadratic cost of aggregation approaches used in prior works. The resulting information bridges a micro-level GNN, which captures local dynamics, and a macro-level transformer that reasons efficiently across segments, achieving a principled balance between accuracy and efficiency. Evaluated on multiple representative benchmark datasets, M4GN improves prediction accuracy by up to 56% while achieving up to 22% faster inference than state-of-the-art baselines.
ZapGPT: Free-form Language Prompting for Simulated Cellular Control
Venue: arXiv cs.AI 2025
Year: 2025

Human language is one of the most expressive tools for conveying intent, yet most artificial or biological systems lack mechanisms to interpret or respond meaningfully to it. Bridging this gap could enable more natural forms of control over complex, decentralized systems. In AI and artificial life, recent work explores how language can specify high-level goals, but most systems still depend on engineered rewards, task-specific supervision, or rigid command sets, limiting generalization to novel instructions. Similar constraints apply in synthetic biology and bioengineering, where the locus of control is often genomic rather than environmental perturbation. A key open question is whether artificial or biological collectives can be guided by free-form natural language alone, without task-specific tuning or carefully designed evaluation metrics. We provide one possible answer here by showing, for the first time, that simple agents' collective behavior can be guided by free-form language prompts: one AI model transforms an imperative prompt into an intervention that is applied to simulated cells; a second AI model scores how well the prompt describes the resulting cellular dynamics; and the former AI model is evolved to improve the scores generated by the latter. Unlike previous work, our method does not require engineered fitness functions or domain-specific prompt design. We show that the evolved system generalizes to unseen prompts without retraining. By treating natural language as a control layer, the system suggests a future in which spoken or written prompts could direct computational, robotic, or biological systems to desired behaviors. This work provides a concrete step toward this vision of AI-biology partnerships, in which language replaces mathematical objective functions, fixed rules, and domain-specific programming.
Feature-Centric Approaches to Android Malware Analysis: A Survey
Venue: arXiv cs.CR 2025
Year: 2025

Sophisticated malware families exploit the openness of the Android platform to infiltrate IoT networks, enabling large-scale disruption, data exfiltration, and denial-of-service attacks. This systematic literature review (SLR) examines cutting-edge approaches to Android malware analysis with direct implications for securing IoT infrastructures. We analyze feature extraction techniques across static, dynamic, hybrid, and graph-based methods, highlighting their trade-offs: static analysis offers efficiency but is easily evaded through obfuscation; dynamic analysis provides stronger resistance to evasive behaviors but incurs high computational costs, often unsuitable for lightweight IoT devices; hybrid approaches balance accuracy with resource considerations; and graph-based methods deliver superior semantic modeling and adversarial robustness. This survey contributes a structured comparison of existing methods, exposes research gaps, and outlines a roadmap for future directions to enhance scalability, adaptability, and long-term security in IoT-driven Android malware detection.